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Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and costefficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. However, UAV-BSs have their limitations and complications, for instance, limited available energy. In addition, when several UAV-BSs are deployed to provide coverage to a specific area, the possibility of inter-UAV collisions and the interference to ground users increase. We propose Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) based methods to deploy UAV-BSs under energy constraints to provide efficient and fair coverage to the ground users, while minimising inter-UAV collisions and interference to ground users. The proposed methods outperform the baseline methods by an average increase of 38.94% in system fairness, 42.54% in individual user coverage, and 15.04% in total system coverage, in comparison with the baseline methods. INDEX TERMS Unmanned aerial vehicles (UAVs), wireless coverage, reinforcement learning.

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Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%-72% and increases the spectral efficiency by 43%-55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.

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